I knew the general story but fumbled the exact decomposition.
Start by defining bias and variance and presenting the expected prediction error decomposition. Then explain how model complexity, training set size, and regularization each shift the bias-variance balance, using concrete examples. Conclude with practical implications for model selection and the role of regularization in achieving optimal trade-off.
Pro tip: Mention that the decomposition assumes squared loss and that for other losses (e.g., 0-1 loss) the trade-off manifests differently, showing depth beyond textbook definitions. Also, relate it to NVIDIA's focus on efficient model deployment: understanding the trade-off helps in choosing models that balance accuracy and inference speed.
Define bias as the error from erroneous assumptions in the learning algorithm (underfitting) and variance as the error from sensitivity to small fluctuations in the training set (overfitting).
State that expected prediction error = bias^2 + variance + irreducible error, and briefly explain each term.
Explain that increasing model complexity typically decreases bias but increases variance, leading to a U-shaped test error curve.
Describe how increasing training set size reduces variance while bias remains relatively constant, thus reducing overall error.
Explain that regularization (e.g., L1/L2) increases bias but decreases variance, helping to control overfitting and navigate the trade-off.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.